library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.1.2
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.1.2
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
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##     filter
## The following object is masked from 'package:graphics':
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##     layout
library(readxl)
read_excel("respons.xlsx")
## # A tibble: 45 × 37
##    Timestamp        `School:` `Name of perso…` `How many stud…` `How many stud…`
##    <chr>            <chr>     <chr>            <chr>            <chr>           
##  1 44361.486879895… Guilford  Karen Thompson   93               6               
##  2 44362.517450625… Sterling… Jennifer Short   217              9               
##  3 44364.353940347… Sugarlan… Rebecca Garcia,… 500              11              
##  4 44368.447873136… Sully El… Colleen O'Neill  123              12              
##  5 44368.686196840… Rolling … Abby Sacco       6                15              
##  6 44369.423427673… Forest G… Shontel Simon    25               10              
##  7 <NA>             <NA>      <NA>             <NA>             <NA>            
##  8 <NA>             <NA>      <NA>             <NA>             <NA>            
##  9 <NA>             <NA>      <NA>             <NA>             <NA>            
## 10 Health pillar    <NA>      How many studen… How many famili… How many famili…
## # … with 35 more rows, and 32 more variables:
## #   `How many students received case management by a social worker?` <chr>,
## #   `How many students participated in support group sessions facilitated by SSWs?` <chr>,
## #   `How many suicide screenings were conducted?` <chr>,
## #   `How many staff received professional development around mental health?` <chr>,
## #   `How did your school assist students and families with mental health needs?` <chr>,
## #   `Key partners your school utilized for mental health services:` <chr>, …
health <- read_excel("respons.xlsx")
subset_health <- health[11:16,c(1,3)]
school <- subset_health$Timestamp
num <- subset_health$`Name of person submitting this report:`
s <- ggplot(subset_health,aes(x=school,y=num,fill=school))+geom_col()+labs(title="Number of Students Assisted with Health and Social Services",x="School",y="Total") + scale_fill_discrete(name = "") + geom_text(aes(label = num, y = num), size = 3, position = position_stack(vjust = 1.01))
ggplotly(s)

Sterling had the largest number of students assisted with health and social services followed by Sugarland and Forest Grove. Sully, Guilford, and Rolling Ridge had below 21 students receieve services.

subset_health2 <- health[11:16,c(1,5)]
school2 <- subset_health2$Timestamp
num2 <- subset_health2$`How many students received socio-cultural assessments?`
num2 <- as.numeric(num2)
h <- ggplot(subset_health,aes(x=school2,y=num2,fill=school))+geom_col()+labs(title="Number of Families Referred to Rental/Housing Assistance",x="School",y="Total") + scale_fill_discrete(name = "") + geom_text(aes(label = num2, y = num2), size = 3, position = position_stack(vjust = 1.01))
ggplotly(h)

Sully and Guilford families between 30 and 47 families receive rental/housing assistance while all other schools had 5 families or less receieve assistance.

subset_health3 <- health[11:16,c(1,6)]
school3 <- subset_health3$Timestamp
num3 <- subset_health3$`How many students received case management by a social worker?`
num3 <- as.numeric(num3)
c <- ggplot(subset_health,aes(x=school3,y=num3,fill=school))+geom_col()+labs(title="Number of Families Who Received Clothing",x="School",y="Total",subtitle="From school closet or community partners") + scale_fill_discrete(name = "")  + geom_text(aes(label = num3, y = num3), size = 3, position = position_stack(vjust = 1.01))
ggplotly(c)

Guilford has a large number of families who received clothing resources followed by Sugarland and Sterling who received around 150 clothing resources. Sully, Rolling Ridge and Forest Grove received a very low number of clothing resources.

subset_health4 <- health[11:16,c(1,10)]
school4 <- subset_health4$Timestamp
num4 <- subset_health4$`How did your school assist students and families with mental health needs?`
num4 <- as.numeric(num4)
y <-ggplot(subset_health,aes(x=school4,y=num4,fill=school))+geom_col()+labs(title="Number of Students Who Participated in Youth Development Activities",x="School",y="Total") + scale_fill_discrete(name = "")  + geom_text(aes(label = num4, y = num4), size = 3, position = position_stack(vjust = 1.01))
ggplotly(y)

Sterling had the largest number of students who participated in youth developent activities followed by Sully. Sugarland and Rolling Ridge only had about 25 students while Guilford had 2 and Forest Grove had none.